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An Efficient Algorithm for Mining Frequent Itemsets within Large Windows over Data Streams
Mahmood Deypir, Mohammad Hadi Sadreddini
Pages - 119 - 125 | Revised - 01-07-2011 | Published - 05-08-2011
Published in International Journal of Data Engineering (IJDE)
MORE INFORMATION
KEYWORDS
Data Stream Mining , Frequent Itemsets, Sliding Window
ABSTRACT
Sliding window is an interesting model for frequent pattern mining over data stream due to handling concept change by considering recent data. In this study, a novel approximate algorithm for frequent itemset mining is proposed which operates in both transactional and time sensitive sliding window model. This algorithm divides the current window into a set of partitions and estimates the support of newly appeared itemsets within the previous partitions of the window. By monitoring essential set of itemsets within incoming data, this algorithm does not waste processing power for itemsets which are not frequent in the current window. Experimental evaluations using both synthetic and real datasets shows the superiority of the proposed algorithm with respect to previously proposed algorithms.
| 1 | Hung, L. N., Thu, T. N. T., & Nguyen, G. C. (2015). An Efficient Algorithm in Mining Frequent Itemsets with Weights over Data Stream Using Tree Data Structure. International Journal of Intelligent Systems and Applications (IJISA), 7(12), 23. |
| 2 | Mala, A., & Ramesh, D. F. (2014, August). Web Log Mining to Enhance Surfing Experience. In Applied Mechanics and Materials (Vol. 626, pp. 7-13). Trans Tech Publications. |
| 3 | Mathai, P. P., & Balan, R. S. An Extensive Review of Significant Researches in Data Mining. |
| 4 | Nguyen, T. T., & Nguyen, P. K. (2013). A New Viewpoint for Mining Frequent Patterns. Editorial Preface, 4(3). |
| 5 | Li Haifeng, Zhang Ning, Zhu Jianming, & Caohuai Hu. (2012) itemsets time-sensitive data stream mining algorithms. Journal of Computers, 35 (11), 2283-2293. |
| 6 | Nguyen, T. T., & Nguyen, P. K. (2012). A new approach for problem of sequential pattern mining. In Computational Collective Intelligence. Technologies and Applications (pp. 51-60). Springer Berlin Heidelberg. |
| 7 | Chandrika, J., & Kumar, K. A. (2012). Frequent Itemset Mining in Transactional Data Streams Based on Quality Control and Resource Adaptation. International Journal of Data Mining & Knowledge Management Process, 2(6), 1. |
| 8 | Bangalore, M. H. S. (2012). Resource adaptive technique for frequent itemset mining in transactional data streams. IJCSNS, 12(10), 87. |
| 1 | Google Scholar  |
| 2 | CiteSeerX |
| 3 | refSeek |
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| 5 | SlideShare |
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| A. Savasere, E. Omiecinski, and S. Navathe, “An efficient algorithm for mining association in large databases”, in Proceeding of the VLDB International Conference on Very Large Databases, pp. 432–444, 1995. | |
| B. Mozafari, H. Thakkar, & C. Zaniolo. “Verifying and mining frequent patterns from large windows over data streams”, Proc. Int. Conf. ICDE, pp.179–188, 2008. | |
| C.-H. Lin, D.-Y. Chiu, Y.-H. Wu, & A.L.P. Chen. “Mining frequent itemsets from data streams with a time-sensitive sliding window”, Proc. SIAM Int. Conf. Data Mining, 2005. | |
| C.K.-S. Leung, & Q.I. Khan. “DSTree: a tree structure for the mining of frequent sets from data streams”, Proc. ICDM, 928–932, 2006. | |
| H. F. Li, S. Y. Lee, & M. K. Shan “An efficient algorithm for mining frequent itemsets over the entire history of data streams” Proc. Int. Workshop on Knowledge Discovery in Data Streams, 2004. | |
| H. Li, & H. Chen. “Mining non-derivable frequent itemsets over data stream”, Data & Knowledge Engineering, vol. 68(5), pp. 481-498, 2009. | |
| H.-F. Li, S.-Y. Lee. “Mining frequent itemsets over data streams using efficient window sliding techniques”, Expert Systems with Applications, vol. 36(2), pp. 1466–1477, 2009. | |
| J. Chang, W. Lee, “Finding recently frequent itemsets adaptively over online transactional data streams”, Information Systems, vol. 31 (8), pp. 849-869, 2006. | |
| J. Han, H. Cheng, D. Xin, & X. Yan. “Frequent pattern mining: current status and future directions”, Data Mining and Knowledge Discovery,vol. 15(1), pp. 55–86, 2007. | |
| J. Han, J. Pei, Y. Yin, & R. Mao. “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Data Mining and Knowledge Discovery, vol. 8(1), pp. 53-87, 2004. | |
| J.H. Chang, & W.S. Lee. “estWin: Online data stream mining of recent frequent itemsets by sliding window method” Journal of Information Science, vol. 31(2), pp. 76–90, 2005. | |
| J.H. Chang, W.S. Lee, “estMax: Tracing Maximal Frequent Itemsets Instantly over Online Transactional Data Streams”, IEEE Transactions on Knowledge and Data Engineering, vol. 21 (10) pp.1418-1431, 2009. | |
| J.X. Yu, Z. Chong, H. Lu, Z. Zhang, Z., & A. Zhou. “A false negative approach to mining frequent itemsets from high speed transactional data streams” Information Sciences, vol. 176(14), pp. 1986–2015, 2006. | |
| R. Agrawal and R. Srikant, “Fast algorithms for mining association rules” in Proc. Int. Conf. on Very Large Databases, pp. 487–499, 1994. | |
| S.K.Tanbeer, C. F. Ahmed, B.-S. Jeong, & Y.-K. Lee. “Sliding window-based frequent pattern mining over data streams”, Information Sciences, vol. 179(22), pp. 3843-3865, 2009. | |
| X. Zhi-Jun, C. Hong, & C. Li. “An efficient algorithm for frequent itemset mining on data streams” Proc. ICDM, 474–491, 2006. | |
| Y. Chi, H. Wang, P.S. Yu, & R.R. Muntz. “Catch the moment: maintaining closed frequent itemsets over a data stream sliding window” Knowledge and Information Systems, 10(3), pp. 265–294, 2006. | |
Mr. Mahmood Deypir
- Iran
mdeypir@gmail.com
Associate Professor Mohammad Hadi Sadreddini
- Iran
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